Skip to main content

AI Toolkit for Healthcare Imaging

Project description

project-monai

Medical Open Network for AI

License CI Build Documentation Status codecov PyPI version

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are:

  • developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
  • creating state-of-the-art, end-to-end training workflows for healthcare imaging;
  • providing researchers with the optimized and standardized way to create and evaluate deep learning models.

Features

The codebase is currently under active development. Please see the technical highlights and What's New of the current milestone release.

  • flexible pre-processing for multi-dimensional medical imaging data;
  • compositional & portable APIs for ease of integration in existing workflows;
  • domain-specific implementations for networks, losses, evaluation metrics and more;
  • customizable design for varying user expertise;
  • multi-GPU data parallelism support.

Installation

To install the current release, you can simply run:

pip install monai

For other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.

Getting Started

MedNIST demo and MONAI for PyTorch Users are available on Colab.

Examples and notebook tutorials are located at Project-MONAI/tutorials.

Technical documentation is available at docs.monai.io.

Contributing

For guidance on making a contribution to MONAI, see the contributing guidelines.

Community

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI's GitHub Discussions tab.

Links

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

monai-weekly-0.9.dev2215.tar.gz (593.6 kB view details)

Uploaded Source

Built Distribution

monai_weekly-0.9.dev2215-py3-none-any.whl (776.3 kB view details)

Uploaded Python 3

File details

Details for the file monai-weekly-0.9.dev2215.tar.gz.

File metadata

  • Download URL: monai-weekly-0.9.dev2215.tar.gz
  • Upload date:
  • Size: 593.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for monai-weekly-0.9.dev2215.tar.gz
Algorithm Hash digest
SHA256 60df9da5badc947177d39d5d2f0b3d416dbac4f496048c0f9e9388bf257e9e1a
MD5 6bbf05a802b7ee1fddf3d9a8495af6fc
BLAKE2b-256 a703848dbd1b24d63f0b770d150799b0a4e90dd6d691cb59c6f5756278cc96dd

See more details on using hashes here.

File details

Details for the file monai_weekly-0.9.dev2215-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-0.9.dev2215-py3-none-any.whl
Algorithm Hash digest
SHA256 99042477d5dbf5643241c98e471d8d14985e3d72b651b759cf7ef63d5b0827e8
MD5 c3abc12bbc04bae5d3aeb59e9c425b93
BLAKE2b-256 16d21ed14676a55cdf73d54ca69034ce387c0aa21eedfe0c33a05df746a6d018

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page